Detection of interest features is a fundamental pre-processing task for visual odometry and visual simultaneous localization and mapping. The combination of line segments and corners is a trend for those applications, and it is challenging to meet real-time constraints. To pursue this goal, this letter presents a novel points and line points feature detection (PLPD) algorithm designed to be highly efficient and accurate for real-time VO-based systems. The proposed algorithm adopts a novel Multi-level Edge Detector (MED) based on gradient intensity and structure tensor that is not confined solely to extracting corners, but rather, it exploits feature-line points properties to extract long-line segments directly without merging collinear fragment segments. The main focus of this work is to fulfill the real-time and embeddability constraints of our proposed method by adopting the GPU-aware software design for a suitable implementation in GPU-based embedded heterogeneous architecture. Based on this, we exhibit extensive benchmarking with other state-of-the-art algorithms for feature-line segment extraction and corner detection, showing the efficiency of the proposed algorithm and ensuring a real-time performance up to 33 FPS using our dataset.

This paper proposes simple and efficient algorithms for implementation of attitude estimation and control based on data fusion using complementary filters taking into account sensors dynamics. First of all, we propose a passive form of the filter by fusing the measured inertial vectors and the gyro measurements in order to reconstruct real inertial vectors which can be used with any algebraic algorithm (TRIAD, QUEST, etc.) that leads to globally asymptotic attitude estimation. Thereafter, the same principle of data fusion is used to address the problem of attitude stabilization. Then, instead of using direct raw measurements in control law we propose a new solution that leads to accurate estimation of inertial vectors by using complementary filters based on sensors dynamics. The stability analysis of the error dynamics based on Lyapunov method proved that almost all trajectories converge asymptotically to the desired equilibrium point. Simulation results show the effectiveness and the performance of the proposed solutions.

In this paper a new method for attitude estimation of rigid body using line-based vision and a Multiplicative Extended Kaiman Filter (MEKF) is developed. A vision-based line-tracking algorithm that allows to detect and to track points and lines along sequence of images without drift is used. From this algorithm, we can get an implicit measure of the lines direction. The latter are then fused with gyro measurements using an observer designed on SO (3) in order to estimate attitude with gyro bias compensation. The gain matrices of the proposed observer are determined based on continuous-time MEKF. The problem of sign ambiguity related to the implicit measure of direction lines is addressed and a correction factor is used to remove this ambiguity. Simulation results has been presented to show the effectiveness of the proposed approach.

This paper addresses the problem of estimation of attitude of accelerated rigid body vehicles moving in 3D-space using IMU and data related to measurement of velocity by GPS. We propose a new observer for attitude estimation based on proportional-integral (PI) compensator along with discontinuous switching function in the estimation of velocity to compensate the effect of linear acceleration. The convergence analysis of the proposed observer is studied using Barbalat's lemma based on a Lyapunov like positive definite function to guarantee asymptotic convergence. The performance of the observer is illustrated by simulation results which show the convergence behaviour of the proposed observer.

This paper presents a nonlinear observer algorithm for attitude estimation that improves the quality of measures obtained by using low-cost inertial measurements (IMU). It is based on sliding mode observer that provides both the estimates of the gyro-bias and the actual attitude of the rigid body. The algorithm was developed in order to address the well-known problem of the weak dynamics of the tilt sensors and magnetometers, which can be modeled by low pass filters, and of the measurement bias of the gyros. In its design the observer uses the real measurements given by the low-cost attitude sensors (inclinometers and magnetometers) and the gyros, the filters modeling the sensors and the kinematics equation of the rigid body. The asymptotic convergence of the estimation of the attitude and bias-gyros was proven using Lyapunov stability method. The effectiveness of the algorithm has been shown from experimental tests using a rotary platform equipped with several sensors with axes of rotation coincide with orientation of the rigid body. Also, tests for comparison with a linear complementary filter are given.

This paper applies the step by step algorithm based on sliding mode observer to estimate tire forces. To define an observer, a model of a four-wheeled vehicle has been developed. The model is a rigid-body model with 10 degrees-of-freedom, including individual wheel speeds. Then a vehicle dynamic state is defined with the tire forces treated as vehicle variables with unknown dynamics. A field of test results is used to demonstrate the validity of this technique.